The case for power management in web servers
Power aware computing
IEEE Transactions on Parallel and Distributed Systems
Multiprocessor Energy-Efficient Scheduling for Real-Time Tasks with Different Power Characteristics
ICPP '05 Proceedings of the 2005 International Conference on Parallel Processing
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Power reduction techniques for microprocessor systems
ACM Computing Surveys (CSUR)
Making scheduling "cool": temperature-aware workload placement in data centers
ATEC '05 Proceedings of the annual conference on USENIX Annual Technical Conference
Power provisioning for a warehouse-sized computer
Proceedings of the 34th annual international symposium on Computer architecture
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
VirtualPower: coordinated power management in virtualized enterprise systems
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
IEEE Transactions on Parallel and Distributed Systems
PowerNap: eliminating server idle power
Proceedings of the 14th international conference on Architectural support for programming languages and operating systems
IEEE Transactions on Parallel and Distributed Systems
Minimizing Energy Consumption for Precedence-Constrained Applications Using Dynamic Voltage Scaling
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Cooperative power-aware scheduling in grid computing environments
Journal of Parallel and Distributed Computing
Energy aware consolidation for cloud computing
HotPower'08 Proceedings of the 2008 conference on Power aware computing and systems
Co-management of power and performance in virtualized distributed environments
GPC'11 Proceedings of the 6th international conference on Advances in grid and pervasive computing
Profit-driven scheduling for cloud services with data access awareness
Journal of Parallel and Distributed Computing
Job allocation strategies for energy-aware and efficient Grid infrastructures
Journal of Systems and Software
The Journal of Supercomputing
A request multiplexing method based on multiple tenants in saas
GPC'12 Proceedings of the 7th international conference on Advances in Grid and Pervasive Computing
Efficient resource management for virtual desktop cloud computing
The Journal of Supercomputing
An economic model for green cloud
Proceedings of the 10th International Workshop on Middleware for Grids, Clouds and e-Science
Journal of Computational Physics
Experimental analysis of task-based energy consumption in cloud computing systems
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
Improving cloud infrastructure utilization through overbooking
Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference
State-of-the-art research study for green cloud computing
The Journal of Supercomputing
Cloud engineering is Search Based Software Engineering too
Journal of Systems and Software
Automated analysis of performance and energy consumption for cloud applications
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
An online parallel scheduling method with application to energy-efficiency in cloud computing
The Journal of Supercomputing
Analysis of virtual machine live-migration as a method for power-capping
The Journal of Supercomputing
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The energy consumption of under-utilized resources, particularly in a cloud environment, accounts for a substantial amount of the actual energy use. Inherently, a resource allocation strategy that takes into account resource utilization would lead to a better energy efficiency; this, in clouds, extends further with virtualization technologies in that tasks can be easily consolidated. Task consolidation is an effective method to increase resource utilization and in turn reduces energy consumption. Recent studies identified that server energy consumption scales linearly with (processor) resource utilization. This encouraging fact further highlights the significant contribution of task consolidation to the reduction in energy consumption. However, task consolidation can also lead to the freeing up of resources that can sit idling yet still drawing power. There have been some notable efforts to reduce idle power draw, typically by putting computer resources into some form of sleep/power-saving mode. In this paper, we present two energy-conscious task consolidation heuristics, which aim to maximize resource utilization and explicitly take into account both active and idle energy consumption. Our heuristics assign each task to the resource on which the energy consumption for executing the task is explicitly or implicitly minimized without the performance degradation of that task. Based on our experimental results, our heuristics demonstrate their promising energy-saving capability.